For medical diagnostic purposes, volume data can be generated for example by a computer tomograph scanner or a magnetic resonance imaging scanner. Trained medical personnel can interpret the “image slices” and can explain their findings to a patient, for example, it may be desirable to explain a diagnosis or a planned surgical procedure to the patient.
To this end, in order to show the information contained in volume data in such a way that even an untrained person can identify elements of interest in the volume, it has been proposed in WO 2016/045701 A1 to apply a type of “cinematic rendering” to show the information as a “3D image”, i.e. a two-dimensional view (shown on a computer monitor, for example) of the three-dimensional volume data, showing the object of interest in a very realistic manner. This technique allows the viewer to observe the three-dimensional “object” (for example the head, thorax, abdomen etc.) from any viewpoint or at any level of detail, and to clearly identify different elements such as bone, muscle, blood vessels, etc.
For this technique of cinematic rendering, Monte Carlo path tracing is applied over several iterations to render the volume data in a three-dimensional image for the chosen parameters. In this technique, many virtual paths are traced in a reverse manner, i.e. originating from pixels of the image plane and travelling through the volume. The colour and intensity of a pixel in the image plane is determined by a global radiance map (defining the direction, colour and brightness of a virtual light source directed at the image) and whether the traced paths (originating from that pixel) are absorbed in the volume, whether they pass through the volume, or whether they undergo volumetric scattering or surface scattering in the volume. A path can undergo several scatter events before leaving the volume, being absorbed by the volume, for example. The fate of each traced path is determined largely by properties of the data volume voxels such as their alpha values from the transfer function. Usually, a global transfer function assigns colour and opacity values to each voxel of the volume.
A user can interact with the rendered three-dimensional “object” to alter the point of view, the level of illumination, the type of tissue to be displayed, etc. For example, the user can switch from a frontal view to a side view; the user can switch between a view in which only bone is displayed to a view in which muscle and other tissue types are also shown, etc. However, changing one or more parameters leads to a problem on account of the progressive nature of the rendering algorithm. When one or more parameters are altered (e.g. changing the viewpoint to “rotate” the object being viewed), the image is initially very noisy.
The image quality only improves after carrying out successive iterations of the path tracing algorithm. However, during this time it is generally not possible to interpret the “noisy” image, particularly when the region of interest includes fine detail or complex structures. This problem can be addressed in a number of ways. For example, the image resolution can be decreased for the first few iterations, or appropriate pseudo-random numbers can be chosen when computing the paths. However, these techniques still suffer from a poor image quality until a sufficient number of iterations have been carried out, so that it is difficult or impossible to correctly interpret regions of the image during this time.